DL is currently one of the mainstream methods in ML [1]}, [2]}and handles ML tasks with high accuracy. DL is based on neural network technology. Neural networks are inspired by the biological neural networks of animal brains and consist of nodes and edges. Nodes are connected to each other by edges, and each edge has a weight. Fig. REF  shows an example of a neural network that predicts the weather for the next day. In the figure, \(x_{i}\) , \(y_{i}\) , and \(z_{i}\)  are nodes, and \(w_{jk}^i\)  represents the weight. The input data, \(x_{1}\) , \(x_{2}\) , and \(x_{3}\) , correspond to today's air temperature, humidity, and weather, respectively. The output data correspond to the probability of the next day's weather: \(y_{1}\)  is sunny, \(y_{2}\)  is cloudy, and \(y_{3}\)  is rain. This model is called a three-layer neural network: \(x_{i}\)  represents the input layer, \(z_{i}\)  represents the hidden layer, and \(y_{i}\)  represents the output layer. \(z_{1}\)  is calculated from the inputs \(x_{1}\) , \(x_{2}\) , and \(x_{3}\)  and the weights \(w_{11}^1\) , \(w_{21}^1\) , and \(w_{31}^1\)  by using a function, e.g. a sigmoid function. The other outputs, \(y_{i}\)  and \(z_{i}\) , are calculated in a similar manner. Before using a neural network, it must be trained to estimate the appropriate weights, \(w_{jk}^i\) , by using weather information from the past. This step is called training, and the weather information used for the training is called the training data/dataset.
